import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor


def 	lowerCamelCase					(	UpperCamelCase     :				str							)   ->    Dict:
		_lowerCamelCase				   =  SwinaSRConfig()

		if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
				_lowerCamelCase				   =  4
		elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
				_lowerCamelCase				   =  4
				_lowerCamelCase				   =  48
				_lowerCamelCase				   =  'pixelshuffle_aux'
		elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
				_lowerCamelCase				   =  [6, 6, 6, 6]
				_lowerCamelCase				   =  60
				_lowerCamelCase				   =  [6, 6, 6, 6]
				_lowerCamelCase				   =  'pixelshuffledirect'
		elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
				_lowerCamelCase				   =  4
				_lowerCamelCase				   =  'nearest+conv'
		elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
				_lowerCamelCase				   =  1
				_lowerCamelCase				   =  1
				_lowerCamelCase				   =  1_26
				_lowerCamelCase				   =  7
				_lowerCamelCase				   =  255.0
				_lowerCamelCase				   =  ''

		return config


def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				Optional[int]							)   ->    List[Any]:
		if "patch_embed.proj" in name and "layers" not in name:
				_lowerCamelCase				   =  name.replace('patch_embed.proj'			,       'embeddings.patch_embeddings.projection'							)
		if "patch_embed.norm" in name:
				_lowerCamelCase				   =  name.replace('patch_embed.norm'			,       'embeddings.patch_embeddings.layernorm'							)
		if "layers" in name:
				_lowerCamelCase				   =  name.replace('layers'			,       'encoder.stages'							)
		if "residual_group.blocks" in name:
				_lowerCamelCase				   =  name.replace('residual_group.blocks'			,       'layers'							)
		if "attn.proj" in name:
				_lowerCamelCase				   =  name.replace('attn.proj'			,       'attention.output.dense'							)
		if "attn" in name:
				_lowerCamelCase				   =  name.replace('attn'			,       'attention.self'							)
		if "norm1" in name:
				_lowerCamelCase				   =  name.replace('norm1'			,       'layernorm_before'							)
		if "norm2" in name:
				_lowerCamelCase				   =  name.replace('norm2'			,       'layernorm_after'							)
		if "mlp.fc1" in name:
				_lowerCamelCase				   =  name.replace('mlp.fc1'			,       'intermediate.dense'							)
		if "mlp.fc2" in name:
				_lowerCamelCase				   =  name.replace('mlp.fc2'			,       'output.dense'							)
		if "q_bias" in name:
				_lowerCamelCase				   =  name.replace('q_bias'			,       'query.bias'							)
		if "k_bias" in name:
				_lowerCamelCase				   =  name.replace('k_bias'			,       'key.bias'							)
		if "v_bias" in name:
				_lowerCamelCase				   =  name.replace('v_bias'			,       'value.bias'							)
		if "cpb_mlp" in name:
				_lowerCamelCase				   =  name.replace('cpb_mlp'			,       'continuous_position_bias_mlp'							)
		if "patch_embed.proj" in name:
				_lowerCamelCase				   =  name.replace('patch_embed.proj'			,       'patch_embed.projection'							)

		if name == "norm.weight":
				_lowerCamelCase				   =  'layernorm.weight'
		if name == "norm.bias":
				_lowerCamelCase				   =  'layernorm.bias'

		if "conv_first" in name:
				_lowerCamelCase				   =  name.replace('conv_first'			,       'first_convolution'							)

		if (
		    "upsample" in name
		    or "conv_before_upsample" in name
		    or "conv_bicubic" in name
		    or "conv_up" in name
		    or "conv_hr" in name
		    or "conv_last" in name
		    or "aux" in name
		):
				# heads
				if "conv_last" in name:
						_lowerCamelCase				   =  name.replace('conv_last'			,       'final_convolution'							)
				if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
						if "conv_before_upsample.0" in name:
								_lowerCamelCase				   =  name.replace('conv_before_upsample.0'			,       'conv_before_upsample'							)
						if "upsample.0" in name:
								_lowerCamelCase				   =  name.replace('upsample.0'			,       'upsample.convolution_0'							)
						if "upsample.2" in name:
								_lowerCamelCase				   =  name.replace('upsample.2'			,       'upsample.convolution_1'							)
						_lowerCamelCase				   =  'upsample.' + name
				elif config.upsampler == "pixelshuffledirect":
						_lowerCamelCase				   =  name.replace('upsample.0.weight'			,       'upsample.conv.weight'							)
						_lowerCamelCase				   =  name.replace('upsample.0.bias'			,       'upsample.conv.bias'							)
				else:
						pass
		else:
				_lowerCamelCase				   =  'swin2sr.' + name

		return name


def 	lowerCamelCase					(	UpperCamelCase     :				Any			,       UpperCamelCase     :				List[str]							)   ->    Dict:
		for key in orig_state_dict.copy().keys():
				_lowerCamelCase				   =  orig_state_dict.pop(UpperCamelCase							)

				if "qkv" in key:
						_lowerCamelCase				   =  key.split('.'							)
						_lowerCamelCase				   =  int(key_split[1]							)
						_lowerCamelCase				   =  int(key_split[4]							)
						_lowerCamelCase				   =  config.embed_dim

						if "weight" in key:
								_lowerCamelCase				   =  val[:dim, :]
								_lowerCamelCase				   =  val[dim : dim * 2, :]
								_lowerCamelCase				   =  val[-dim:, :]
						else:
								_lowerCamelCase				   =  val[:dim]
								_lowerCamelCase				   =  val[dim : dim * 2]
								_lowerCamelCase				   =  val[-dim:]
						pass
				else:
						_lowerCamelCase				   =  val

		return orig_state_dict


def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]			,       UpperCamelCase     :				str			,       UpperCamelCase     :				Optional[Any]							)   ->    List[Any]:
		_lowerCamelCase				   =  get_config(UpperCamelCase							)
		_lowerCamelCase				   =  SwinaSRForImageSuperResolution(UpperCamelCase							)
		model.eval()

		_lowerCamelCase				   =  torch.hub.load_state_dict_from_url(UpperCamelCase			,       map_location='cpu'							)
		_lowerCamelCase				   =  convert_state_dict(UpperCamelCase			,       UpperCamelCase							)
		_lowerCamelCase   ,							_lowerCamelCase				   =  model.load_state_dict(UpperCamelCase			,       strict=UpperCamelCase							)

		if len(UpperCamelCase							) > 0:
				raise ValueError('Missing keys when converting: {}'.format(UpperCamelCase							)							)
		for key in unexpected_keys:
				if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
						raise ValueError(F"""Unexpected key {key} in state_dict"""							)

    # verify values
		_lowerCamelCase				   =  'https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'
		_lowerCamelCase				   =  Image.open(requests.get(UpperCamelCase			,       stream=UpperCamelCase							).raw							).convert('RGB'							)
		_lowerCamelCase				   =  SwinaSRImageProcessor()
		# pixel_values = processor(image, return_tensors="pt").pixel_values

		_lowerCamelCase				   =  1_26 if 'Jpeg' in checkpoint_url else 2_56
		_lowerCamelCase				   =  Compose(
		    [
		        Resize((image_size, image_size)							),
		        ToTensor(),
		        Normalize(mean=[0.485, 0.456, 0.406]			,       std=[0.229, 0.224, 0.225]							),
		    ]							)
		_lowerCamelCase				   =  transforms(UpperCamelCase							).unsqueeze(0							)

		if config.num_channels == 1:
				_lowerCamelCase				   =  pixel_values[:, 0, :, :].unsqueeze(1							)

		_lowerCamelCase				   =  model(UpperCamelCase							)

		# assert values
		if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
				_lowerCamelCase				   =  torch.Size([1, 3, 5_12, 5_12]							)
				_lowerCamelCase				   =  torch.tensor(
				    [[-0.7_087, -0.7_138, -0.6_721], [-0.8_340, -0.8_095, -0.7_298], [-0.9_149, -0.8_414, -0.7_940]]							)
		elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
				_lowerCamelCase				   =  torch.Size([1, 3, 10_24, 10_24]							)
				_lowerCamelCase				   =  torch.tensor(
				    [[-0.7_775, -0.8_105, -0.8_933], [-0.7_764, -0.8_356, -0.9_225], [-0.7_976, -0.8_686, -0.9_579]]							)
		elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
				# TODO values didn't match exactly here
				_lowerCamelCase				   =  torch.Size([1, 3, 10_24, 10_24]							)
				_lowerCamelCase				   =  torch.tensor(
				    [[-0.8_035, -0.7_504, -0.7_491], [-0.8_538, -0.8_124, -0.7_782], [-0.8_804, -0.8_651, -0.8_493]]							)
		elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
				_lowerCamelCase				   =  torch.Size([1, 3, 5_12, 5_12]							)
				_lowerCamelCase				   =  torch.tensor(
				    [[-0.7_669, -0.8_662, -0.8_767], [-0.8_810, -0.9_962, -0.9_820], [-0.9_340, -1.0_322, -1.1_149]]							)
		elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
				_lowerCamelCase				   =  torch.Size([1, 3, 10_24, 10_24]							)
				_lowerCamelCase				   =  torch.tensor(
				    [[-0.5_238, -0.5_557, -0.6_321], [-0.6_016, -0.5_903, -0.6_391], [-0.6_244, -0.6_334, -0.6_889]]							)

		assert (
		    outputs.reconstruction.shape == expected_shape
		), F"""Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"""
		assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]			,       UpperCamelCase			,       atol=1e-3							)
		print('Looks ok!'							)

		_lowerCamelCase				   =  {
		    'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth': (
		        'swin2SR-classical-sr-x2-64'
		    ),
		    'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth': (
		        'swin2SR-classical-sr-x4-64'
		    ),
		    'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth': (
		        'swin2SR-compressed-sr-x4-48'
		    ),
		    'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth': (
		        'swin2SR-lightweight-x2-64'
		    ),
		    'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth': (
		        'swin2SR-realworld-sr-x4-64-bsrgan-psnr'
		    ),
		}
		_lowerCamelCase				   =  url_to_name[checkpoint_url]

		if pytorch_dump_folder_path is not None:
				print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""							)
				model.save_pretrained(UpperCamelCase							)
				print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
				processor.save_pretrained(UpperCamelCase							)

		if push_to_hub:
				model.push_to_hub(F"""caidas/{model_name}"""							)
				processor.push_to_hub(F"""caidas/{model_name}"""							)


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--checkpoint_url',
				    default='https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth',
				    type=str,
				    help='URL of the original Swin2SR checkpoint you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
				)
				parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the converted model to the hub.')

				A						=			parser.parse_args()
				convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    float:
		return base * power(UpperCamelCase			,       (exponent - 1)							) if exponent else 1


if __name__ == "__main__":
				print('Raise base to the power of exponent using recursion...')
				A						=			int(input('Enter the base: ').strip())
				A						=			int(input('Enter the exponent: ').strip())
				A						=			power(base, abs(exponent))
				if exponent < 0:  # power() does not properly deal w/ negative exponents
								A						=			1 / result
				print(F'''{base} to the power of {exponent} is {result}''')